Psychological inoculation improves resilience against misinformation on social media

Online misinformation continues to have adverse consequences for society. Inoculation theory has been put forward as a way to reduce susceptibility to misinformation by informing people about how they might be misinformed, but its scalability has been elusive both at a theoretical level and a practical level. We developed five short videos that inoculate people against manipulation techniques commonly used in misinformation: emotionally manipulative language, incoherence, false dichotomies, scapegoating, and ad hominem attacks. In seven preregistered studies, i.e., six randomized controlled studies (n = 6464) and an ecologically valid field study on YouTube (n = 22,632), we find that these videos improve manipulation technique recognition, boost confidence in spotting these techniques, increase people’s ability to discern trustworthy from untrustworthy content, and improve the quality of their sharing decisions. These effects are robust across the political spectrum and a wide variety of covariates. We show that psychological inoculation campaigns on social media are effective at improving misinformation resilience at scale.

v We also conducted a series of exploratory analyses of the sharing measure that excluded "never sharers", i.e., participants who answered "strongly disagree" on the sharing measure for all stimuli. The exclusion of participants who would never share either manipulative or neutral posts with people in their network follows guidelines established in previous work by Pennycook et al. (54); these results are highly similar to those reported below, and can be found in Table S35. vi Technique recognition for control group participants is higher in some studies than others: an ANOVA shows that technique recognition among control group participants differs significantly across studies (F(4,2787) = 84.1, p < 0.001, η² = 0.109, d = 0.70), and is highest in the scapegoating study, for which we also report a descriptively lower effect size than for the other four studies (see Table S33). We find the same pattern for trustworthiness (see Table S34). Thus, it is possible that compared to the other manipulation techniques, participants were better at spotting scapegoating in social media content even without an intervention, which may explain the lower effect size and non-significant effects for the trustworthiness and sharing intentions measures.
vii In addition to the preregistered moderation analyses, we conducted a series of (nonpreregistered) ANOVAs with technique recognition as the dependent variable and condition (inoculationcontrol) and political ideology (converted from a 7-point scale to "left", "moderate" and "right"), "bullshit receptivity" (converted to "high" and "low"), and analytical thinking (also converted to "high" and "low"), respectively, as independent variables, separately for each study. We find that technique recognition is significantly higher in the inoculation condition than in the control condition for those on the left and right, as well as for moderates (all p-values < 0.003), for participants with high and low "bullshit receptivity" (all p-values < 0.01), and for participants with high and low analytical thinking scores (all p-values < 0.015), with two exceptions: there is no difference between conditions among moderates for the "emotional language" study (p = 0.685), and for moderates and conservatives for the "scapegoating" study (p = 0.148 and p = 0.652, respectively). See Tables S23-S27, S38-S43, and S52-S54 for the regression tables, as well as Figures S2-S6. viii This type of survey is called a YouTube "brand lift" survey. For more information about how this works, see: https://support.google.com/youtube/answer/4574026?hl=en. ix We note that Item 1 for the emotional language survey ("What this airline did for its passengers will make you tear up -SO heart-warming.") is different from the other two headlines in that it does not make use of negative emotions; as the emotional language video specifically inoculates people against the use of negative emotions such as fear, anger, or outrage, it is possible that this discrepancy between the headline and the lessons learned in the video explains the lack of a significant effect for Item 1.
x As an example, one of the manipulative (incoherent) posts from the incoherence study reads: "The 'scientific consensus' on global warming is a myth. Only a few scientists dare go against the grain. They are our heroes, and they should be celebrated". This post is incoherent because it simultaneously asserts that climate change consensus does not exist and that there are only a few scientists who disagree with the consensus. 34 Its non-manipulative counterpart reads "While there is an ongoing discussion about the exact level of agreement, approximately 97% of scientists agree that anthropogenic climate change is happening". For the emotional language study, we used real-world examples of emotionally manipulative social media content as stimuli, following Brady et al. (22), and conducted a stimuli validation test using a sentiment analysis library to ensure that the manipulative stimuli capture the intended dimension of emotionality and that the neutral stimuli do not; please see the Supplementary Analyses section for further details.

Robustness checks
As preregistered, we conducted a series of linear regressions with robust standard errors at the rating level for the technique recognition, trustworthiness and sharing measures, clustered on study participants and stimuli (manipulative vs neutral), following the approach laid out by Pennycook et al. (54) The regression tables can be found in Tables S28-S32. The results show that the findings reported in the main body are robust, except for one: trustworthiness discernment for the incoherence video is significant when conducting a Student's (p = 0.002) and Bayesian t-test (BF10 = 7.876, indicating strong support in favor of hypothesis H3 (55)), but not when doing a linear regression at the rating level (p = 0.168).
In addition (although not preregistered), we provide Bayesian t-test results alongside the standard Student's t-tests for the averaged manipulative, neutral and discernment scores, as well as for all individual stimuli. In line with standard practices for reporting Bayesian statistics, we used a Cauchy prior, centered around 0, with a width parameter of 0.707, representing an 80% chance that the observed effect sizes are between -2 and 2, as recommended by van Doorn et al (55). Similarly, a Bayes Factor10 (BF10) lower than 3 is considered weak support for the directional hypothesis; between 3 and 5 is considered medium support, and > 5 strong support (55). These Bayesian analyses support the findings reported in the main body; see Supplementary Tables S3-S22.
Finally, because it is known that many people on social media never share news or other information, we conducted a series of exploratory analyses of the sharing measure that excluded "never sharers", i.e., participants who answered "strongly disagree" (1 out of 7) on the sharing measure for all stimuli. The exclusion of participants who would never share either manipulative  Figure S1 shows that the emotional-manipulative social media posts use emotional language, negative emotion, fear-based language, hate-based language, and language related to suffering, whereas the non-emotional (neutral) posts contain no language related to these categories (except negative emotion). These results confirm that our test stimuli capture the intended dimension of (negative) emotionality. Figure S2.  technique recognition (Diff-Technique, or technique recognition), broken down by political ideology (where 1-3 on a 7-point scale is converted to "left", 4 is "moderate" and 5-7 is "right"). For the full moderation analysis, see Tables S38 and S39. Figure created using Jamovi (www.jamovi.org). Figure S3.  technique recognition (Diff-Technique, or technique recognition), broken down by "bullshit receptivity" (where below-average "bullshit receptivity" scores are converted to "low" and above-average scores to "high"). For the full moderation analysis, see Tables S40 and S41. Figure created using Jamovi (www.jamovi.org). Figure S4.  technique recognition (Diff-Technique, or technique recognition), broken down by analytical thinking (where a score of 0 or 1 out of 3 is converted to "low" and a score of 2 or 3 out of 3 to "high"). For the full moderation analysis, see Tables S42 and S43. Figure created using Jamovi (www.jamovi.org).

Figure S6.
Study 6: technique discernment, trustworthiness discernment, and sharing discernment, as moderated by Veracity Discernment Ability (the ability to discern true from false news headlines), according to the 20-item Misinformation Susceptibility Test (41). See also Table S54.   Tables   Table S1a. Sample composition for Studies 1-6 (demographics).    Table S4. Study 1 (emotional language): confidence measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S5. Study 1 (emotional language): trustworthiness measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S6. Study 1 (emotional language): sharing intentions measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S7. Study 2 (incoherence): technique recognition measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S8. Study 2 (incoherence): confidence measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S9. Study 2 (incoherence): trustworthiness measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S10. Study 2 (incoherence): sharing intentions measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S11. Study 3 (false dichotomies): technique recognition measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S12. Study 3 (false dichotomies): confidence measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S13. Study 3 (false dichotomies): trustworthiness measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S14. Study 3 (false dichotomies): sharing intentions measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S15. Study 4 (scapegoating): technique recognition measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S16. Study 4 (scapegoating): confidence measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S17. Study 4 (scapegoating): trustworthiness measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S18. Study 4 (scapegoating): sharing intentions measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S19. Study 5 (ad hominem): technique recognition measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S20. Study 5 (ad hominem): confidence measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S21. Study 5 (ad hominem): trustworthiness measure item-level results (independent samples and Bayesian t-tests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.  Table S22. Study 5 (ad hominem): sharing measure item-level results (independent samples and Bayesian ttests). Note: Bayesian prior used is 0.707 (representing an 80% chance that the effect size is between -2 and 2). "-C-" denotes a manipulative post's matched neutral control.        Table S36. Studies 1-5: Linear regressions with "Fake-Confidence" (confidence in technique recognition for the manipulative/misinformation stimuli) as dependent variable and perceived use of a manipulation technique in manipulative stimuli (Fake-Manipulativeness, Fake-Incoherence, Fake-Dichotomy, Fake-Scapegoating and Fake-AdHominem) and condition (inoculationcontrol) as independent variables. Note that in all 5 studies, the perceived use of a technique in manipulative social media content is significantly and positively correlated with participants' confidence in recognizing these techniques, when controlling for the condition that participants were assigned to.  Table S37. Studies 1-5: Linear regressions with "Control-Confidence" (confidence in technique recognition for the non-manipulative/neutral stimuli) as dependent variable and perceived use of a manipulation technique in non-manipulative stimuli (Control-Manipulativeness, Control-Incoherence, Control-Dichotomy, Control-Scapegoating and Control-AdHominem) and condition (inoculationcontrol) as independent variables. Note that in all 5 studies, the perceived use of a technique in non-manipulative social media content is significantly and negatively correlated with participants' confidence in recognizing these techniques, when controlling for the condition that participants were assigned to.  Table S38. Studies 1-5: ANOVAs for technique recognition (Diff-Technique) with the converted political ideology variable (leftmoderateright) and condition (inoculationcontrol) as independent variables. Significant interactions between political ideology and condition are marked in bold. See also Figure S2 and Table S39 for the Tukey post-hoc tests.  Table S39. Tukey post-hoc tests for technique recognition (Diff-Technique) with the converted political ideology variable (leftmoderateright) and condition (inoculationcontrol) as independent variables. Relevant p-values for differences between inoculation and control conditions for the same political ideology are marked in bold. Note that the only non-significant difference between inoculation and control condition is for moderates for the "emotional language" study, and for moderates and right-wingers for the "scapegoating" study. See also Figure S2 and Table S38.  Table S40. Studies 1-5: ANOVAs for technique recognition (Diff-Technique) with the converted "bullshit receptivity" variable (high -low) and condition (inoculationcontrol) as independent variables. Significant interactions between "bullshit receptivity" and condition are marked in bold. See also Figure S3 and  Table S41. Studies 1-5: Tukey post-hoc tests for technique recognition (Diff-Technique) with the converted "bullshit receptivity" variable (high -low) and condition (inoculationcontrol) as independent variables. Relevant p-values for differences between inoculation and control conditions for the same levels of "bullshit receptivity" are marked in bold. Note that technique recognition is significantly higher for the inoculation condition in all studies for both high-and low levels of "bullshit receptivity". See also Figure S3 and Table S40.  Table S42. Studies 1-5: ANOVAs for technique recognition (Diff-Technique) with the converted analytical thinking variable (high -low) and condition (inoculationcontrol) as independent variables. See also Figure S4 and  Table S43. Studies 1-5: Tukey post-hoc tests for technique recognition (Diff-Technique) with the converted analytical thinking variable (high -low) and condition (inoculationcontrol) as independent variables. Relevant p-values for differences between inoculation and control conditions for the same levels of analytical thinking are marked in bold. Note that technique recognition is significantly higher for the inoculation condition in all studies for both high and low levels of analytical thinking. See also Figure S4 and Table S42.  Table S50. Study 6 (emotional language replication study): ANOVAs for manipulativeness (technique recognition), trustworthiness, and sharing discernment as predicted by condition (controlinoculation) and outcome measure response order (manipulativenesstrustworthinesssharing; trustworthinessmanipulativenesssharing; or sharingtrustworthinessmanipulativeness).  Table S51. Study 6 (emotional language replication study): Student's and Bayesian t-tests for technique recognition, trustworthiness, and sharing, separated by outcome measure response order. Note: "Fake" denotes the averaged scores (per participant) for the manipulative stimuli; "Real" for the non-manipulative stimuli. MTS = manipulativeness-trustworthiness-sharing; TSM = trustworthiness-sharing-manipulativeness; SMT = sharing-manipulativeness-trustworthiness.  Table S52. Study 6 (emotional language replication study): linear regression for manipulativeness, trustworthiness and sharing discernment, as predicted by condition (controlinoculation) and the five personality dimensions from the 10-item Personality Inventory.  Table S53. Study 6 (emotional language replication study): linear regression for manipulativeness, trustworthiness and sharing discernment, as predicted by condition (controlinoculation) and actively open-minded thinking (AOT). See also Figure S5.  Table S54. Study 6 (emotional language replication study): linear regression for manipulativeness, trustworthiness and sharing discernment, as predicted by condition (controlinoculation) and Veracity Discernment Ability (MIST20_VDA, i.e., ability to distinguish true from false headlines), as measured by the 20-item Misinformation Susceptibility Test (MIST). See also Figure S6.